{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from HighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = HighwayNet(drop = 0.2).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.2544 Top-1 准确率: 0.0596 Top-5 准确率: 0.2023\n",
      "val 损失: 4.1830 Top-1 准确率: 0.0673 Top-5 准确率: 0.2110\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.8702 Top-1 准确率: 0.1211 Top-5 准确率: 0.3290\n",
      "val 损失: 4.0321 Top-1 准确率: 0.0899 Top-5 准确率: 0.2751\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.5175 Top-1 准确率: 0.1867 Top-5 准确率: 0.4320\n",
      "val 损失: 3.4742 Top-1 准确率: 0.1659 Top-5 准确率: 0.4214\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2337 Top-1 准确率: 0.2442 Top-5 准确率: 0.5066\n",
      "val 损失: 2.6453 Top-1 准确率: 0.3145 Top-5 准确率: 0.6373\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 3.0179 Top-1 准确率: 0.2889 Top-5 准确率: 0.5533\n",
      "val 损失: 2.5165 Top-1 准确率: 0.3404 Top-5 准确率: 0.6711\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.8342 Top-1 准确率: 0.3266 Top-5 准确率: 0.5953\n",
      "val 损失: 2.3750 Top-1 准确率: 0.3765 Top-5 准确率: 0.7048\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7036 Top-1 准确率: 0.3553 Top-5 准确率: 0.6204\n",
      "val 损失: 2.3385 Top-1 准确率: 0.3851 Top-5 准确率: 0.7021\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5683 Top-1 准确率: 0.3888 Top-5 准确率: 0.6482\n",
      "val 损失: 2.1910 Top-1 准确率: 0.4243 Top-5 准确率: 0.7320\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4644 Top-1 准确率: 0.4110 Top-5 准确率: 0.6694\n",
      "val 损失: 2.1323 Top-1 准确率: 0.4302 Top-5 准确率: 0.7479\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3787 Top-1 准确率: 0.4315 Top-5 准确率: 0.6893\n",
      "val 损失: 2.0709 Top-1 准确率: 0.4520 Top-5 准确率: 0.7587\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3048 Top-1 准确率: 0.4498 Top-5 准确率: 0.7037\n",
      "val 损失: 2.2023 Top-1 准确率: 0.4187 Top-5 准确率: 0.7279\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2516 Top-1 准确率: 0.4575 Top-5 准确率: 0.7169\n",
      "val 损失: 2.3478 Top-1 准确率: 0.3902 Top-5 准确率: 0.7107\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1817 Top-1 准确率: 0.4727 Top-5 准确率: 0.7307\n",
      "val 损失: 1.7942 Top-1 准确率: 0.5057 Top-5 准确率: 0.8137\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1340 Top-1 准确率: 0.4847 Top-5 准确率: 0.7430\n",
      "val 损失: 1.9225 Top-1 准确率: 0.4856 Top-5 准确率: 0.7882\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0722 Top-1 准确率: 0.5001 Top-5 准确率: 0.7539\n",
      "val 损失: 1.7102 Top-1 准确率: 0.5340 Top-5 准确率: 0.8172\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0441 Top-1 准确率: 0.5065 Top-5 准确率: 0.7628\n",
      "val 损失: 1.7135 Top-1 准确率: 0.5378 Top-5 准确率: 0.8173\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9922 Top-1 准确率: 0.5182 Top-5 准确率: 0.7719\n",
      "val 损失: 1.7181 Top-1 准确率: 0.5310 Top-5 准确率: 0.8230\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9672 Top-1 准确率: 0.5225 Top-5 准确率: 0.7788\n",
      "val 损失: 1.7273 Top-1 准确率: 0.5302 Top-5 准确率: 0.8244\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9171 Top-1 准确率: 0.5339 Top-5 准确率: 0.7934\n",
      "val 损失: 1.7080 Top-1 准确率: 0.5378 Top-5 准确率: 0.8215\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8846 Top-1 准确率: 0.5428 Top-5 准确率: 0.7960\n",
      "val 损失: 1.4468 Top-1 准确率: 0.6000 Top-5 准确率: 0.8697\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8480 Top-1 准确率: 0.5513 Top-5 准确率: 0.8074\n",
      "val 损失: 1.5763 Top-1 准确率: 0.5684 Top-5 准确率: 0.8394\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8297 Top-1 准确率: 0.5531 Top-5 准确率: 0.8100\n",
      "val 损失: 1.5041 Top-1 准确率: 0.5879 Top-5 准确率: 0.8529\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7785 Top-1 准确率: 0.5671 Top-5 准确率: 0.8211\n",
      "val 损失: 1.5714 Top-1 准确率: 0.5709 Top-5 准确率: 0.8406\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7572 Top-1 准确率: 0.5699 Top-5 准确率: 0.8255\n",
      "val 损失: 1.5313 Top-1 准确率: 0.5813 Top-5 准确率: 0.8542\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7305 Top-1 准确率: 0.5763 Top-5 准确率: 0.8317\n",
      "val 损失: 1.5025 Top-1 准确率: 0.5887 Top-5 准确率: 0.8560\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7012 Top-1 准确率: 0.5834 Top-5 准确率: 0.8384\n",
      "val 损失: 1.3378 Top-1 准确率: 0.6293 Top-5 准确率: 0.8787\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6853 Top-1 准确率: 0.5837 Top-5 准确率: 0.8409\n",
      "val 损失: 1.4286 Top-1 准确率: 0.6052 Top-5 准确率: 0.8714\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6523 Top-1 准确率: 0.5926 Top-5 准确率: 0.8479\n",
      "val 损失: 1.3081 Top-1 准确率: 0.6380 Top-5 准确率: 0.8808\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6192 Top-1 准确率: 0.6001 Top-5 准确率: 0.8527\n",
      "val 损失: 1.3838 Top-1 准确率: 0.6179 Top-5 准确率: 0.8776\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6032 Top-1 准确率: 0.6059 Top-5 准确率: 0.8585\n",
      "val 损失: 1.3070 Top-1 准确率: 0.6340 Top-5 准确率: 0.8825\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5775 Top-1 准确率: 0.6121 Top-5 准确率: 0.8640\n",
      "val 损失: 1.3250 Top-1 准确率: 0.6345 Top-5 准确率: 0.8793\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5462 Top-1 准确率: 0.6162 Top-5 准确率: 0.8671\n",
      "val 损失: 1.3026 Top-1 准确率: 0.6418 Top-5 准确率: 0.8865\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5367 Top-1 准确率: 0.6188 Top-5 准确率: 0.8732\n",
      "val 损失: 1.2541 Top-1 准确率: 0.6520 Top-5 准确率: 0.8879\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5197 Top-1 准确率: 0.6238 Top-5 准确率: 0.8765\n",
      "val 损失: 1.4426 Top-1 准确率: 0.6056 Top-5 准确率: 0.8679\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4814 Top-1 准确率: 0.6326 Top-5 准确率: 0.8821\n",
      "val 损失: 1.2885 Top-1 准确率: 0.6493 Top-5 准确率: 0.8840\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4789 Top-1 准确率: 0.6333 Top-5 准确率: 0.8828\n",
      "val 损失: 1.4178 Top-1 准确率: 0.6190 Top-5 准确率: 0.8655\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4533 Top-1 准确率: 0.6400 Top-5 准确率: 0.8886\n",
      "val 损失: 1.2860 Top-1 准确率: 0.6436 Top-5 准确率: 0.8877\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4309 Top-1 准确率: 0.6437 Top-5 准确率: 0.8898\n",
      "val 损失: 1.4158 Top-1 准确率: 0.6227 Top-5 准确率: 0.8736\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4152 Top-1 准确率: 0.6467 Top-5 准确率: 0.8973\n",
      "val 损失: 1.4148 Top-1 准确率: 0.6173 Top-5 准确率: 0.8682\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4124 Top-1 准确率: 0.6488 Top-5 准确率: 0.8991\n",
      "val 损失: 1.3268 Top-1 准确率: 0.6413 Top-5 准确率: 0.8815\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3841 Top-1 准确率: 0.6560 Top-5 准确率: 0.9043\n",
      "val 损失: 1.3309 Top-1 准确率: 0.6373 Top-5 准确率: 0.8809\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3699 Top-1 准确率: 0.6577 Top-5 准确率: 0.9058\n",
      "val 损失: 1.1572 Top-1 准确率: 0.6844 Top-5 准确率: 0.9064\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3492 Top-1 准确率: 0.6621 Top-5 准确率: 0.9118\n",
      "val 损失: 1.1755 Top-1 准确率: 0.6767 Top-5 准确率: 0.9033\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3245 Top-1 准确率: 0.6691 Top-5 准确率: 0.9139\n",
      "val 损失: 1.1984 Top-1 准确率: 0.6751 Top-5 准确率: 0.8989\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3326 Top-1 准确率: 0.6664 Top-5 准确率: 0.9146\n",
      "val 损失: 1.1704 Top-1 准确率: 0.6758 Top-5 准确率: 0.9038\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3125 Top-1 准确率: 0.6710 Top-5 准确率: 0.9186\n",
      "val 损失: 1.3103 Top-1 准确率: 0.6483 Top-5 准确率: 0.8858\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2802 Top-1 准确率: 0.6799 Top-5 准确率: 0.9217\n",
      "val 损失: 1.1993 Top-1 准确率: 0.6725 Top-5 准确率: 0.9033\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2865 Top-1 准确率: 0.6770 Top-5 准确率: 0.9257\n",
      "val 损失: 1.1936 Top-1 准确率: 0.6804 Top-5 准确率: 0.9020\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2681 Top-1 准确率: 0.6824 Top-5 准确率: 0.9270\n",
      "val 损失: 1.2057 Top-1 准确率: 0.6740 Top-5 准确率: 0.8985\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2510 Top-1 准确率: 0.6838 Top-5 准确率: 0.9296\n",
      "val 损失: 1.2999 Top-1 准确率: 0.6594 Top-5 准确率: 0.8890\n",
      "\n",
      "训练完成，耗时 98m 17s\n",
      "最佳验证集准确率: 0.684400\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 1.1809 Top-1 准确率: 0.7043 Top-5 准确率: 0.9264\n",
      "val 损失: 0.9868 Top-1 准确率: 0.7247 Top-5 准确率: 0.9243\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 1.1096 Top-1 准确率: 0.7212 Top-5 准确率: 0.9406\n",
      "val 损失: 0.9751 Top-1 准确率: 0.7275 Top-5 准确率: 0.9270\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0865 Top-1 准确率: 0.7279 Top-5 准确率: 0.9457\n",
      "val 损失: 0.9620 Top-1 准确率: 0.7315 Top-5 准确率: 0.9285\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0633 Top-1 准确率: 0.7334 Top-5 准确率: 0.9511\n",
      "val 损失: 0.9732 Top-1 准确率: 0.7295 Top-5 准确率: 0.9244\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0537 Top-1 准确率: 0.7348 Top-5 准确率: 0.9537\n",
      "val 损失: 0.9495 Top-1 准确率: 0.7347 Top-5 准确率: 0.9303\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0398 Top-1 准确率: 0.7381 Top-5 准确率: 0.9552\n",
      "val 损失: 0.9530 Top-1 准确率: 0.7351 Top-5 准确率: 0.9290\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0167 Top-1 准确率: 0.7437 Top-5 准确率: 0.9580\n",
      "val 损失: 0.9585 Top-1 准确率: 0.7311 Top-5 准确率: 0.9291\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0143 Top-1 准确率: 0.7434 Top-5 准确率: 0.9599\n",
      "val 损失: 0.9412 Top-1 准确率: 0.7380 Top-5 准确率: 0.9307\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9941 Top-1 准确率: 0.7484 Top-5 准确率: 0.9610\n",
      "val 损失: 0.9655 Top-1 准确率: 0.7304 Top-5 准确率: 0.9279\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0030 Top-1 准确率: 0.7448 Top-5 准确率: 0.9625\n",
      "val 损失: 0.9498 Top-1 准确率: 0.7332 Top-5 准确率: 0.9284\n",
      "\n",
      "训练完成，耗时 19m 60s\n",
      "最佳验证集准确率: 0.738000\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9999 Top-1 准确率: 0.7479 Top-5 准确率: 0.9608\n",
      "val 损失: 0.9404 Top-1 准确率: 0.7374 Top-5 准确率: 0.9307\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9933 Top-1 准确率: 0.7492 Top-5 准确率: 0.9629\n",
      "val 损失: 0.9411 Top-1 准确率: 0.7388 Top-5 准确率: 0.9301\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9892 Top-1 准确率: 0.7487 Top-5 准确率: 0.9634\n",
      "val 损失: 0.9477 Top-1 准确率: 0.7373 Top-5 准确率: 0.9289\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9831 Top-1 准确率: 0.7491 Top-5 准确率: 0.9657\n",
      "val 损失: 0.9368 Top-1 准确率: 0.7382 Top-5 准确率: 0.9286\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9772 Top-1 准确率: 0.7515 Top-5 准确率: 0.9666\n",
      "val 损失: 0.9353 Top-1 准确率: 0.7400 Top-5 准确率: 0.9305\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9812 Top-1 准确率: 0.7500 Top-5 准确率: 0.9667\n",
      "val 损失: 0.9480 Top-1 准确率: 0.7390 Top-5 准确率: 0.9291\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9651 Top-1 准确率: 0.7539 Top-5 准确率: 0.9682\n",
      "val 损失: 0.9694 Top-1 准确率: 0.7331 Top-5 准确率: 0.9268\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9541 Top-1 准确率: 0.7563 Top-5 准确率: 0.9701\n",
      "val 损失: 0.9475 Top-1 准确率: 0.7400 Top-5 准确率: 0.9279\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9502 Top-1 准确率: 0.7570 Top-5 准确率: 0.9727\n",
      "val 损失: 0.9324 Top-1 准确率: 0.7429 Top-5 准确率: 0.9299\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9402 Top-1 准确率: 0.7594 Top-5 准确率: 0.9706\n",
      "val 损失: 0.9362 Top-1 准确率: 0.7422 Top-5 准确率: 0.9308\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9453 Top-1 准确率: 0.7575 Top-5 准确率: 0.9733\n",
      "val 损失: 0.9573 Top-1 准确率: 0.7362 Top-5 准确率: 0.9275\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9330 Top-1 准确率: 0.7615 Top-5 准确率: 0.9733\n",
      "val 损失: 0.9479 Top-1 准确率: 0.7391 Top-5 准确率: 0.9280\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9383 Top-1 准确率: 0.7579 Top-5 准确率: 0.9748\n",
      "val 损失: 0.9693 Top-1 准确率: 0.7356 Top-5 准确率: 0.9273\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9209 Top-1 准确率: 0.7623 Top-5 准确率: 0.9754\n",
      "val 损失: 0.9429 Top-1 准确率: 0.7438 Top-5 准确率: 0.9298\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9181 Top-1 准确率: 0.7638 Top-5 准确率: 0.9757\n",
      "val 损失: 0.9519 Top-1 准确率: 0.7411 Top-5 准确率: 0.9269\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9100 Top-1 准确率: 0.7651 Top-5 准确率: 0.9762\n",
      "val 损失: 0.9469 Top-1 准确率: 0.7414 Top-5 准确率: 0.9274\n",
      "\n",
      "第 16/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9193 Top-1 准确率: 0.7626 Top-5 准确率: 0.9772\n",
      "val 损失: 0.9570 Top-1 准确率: 0.7411 Top-5 准确率: 0.9297\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8966 Top-1 准确率: 0.7675 Top-5 准确率: 0.9785\n",
      "val 损失: 0.9638 Top-1 准确率: 0.7380 Top-5 准确率: 0.9265\n",
      "\n",
      "第 18/29 轮训练\n",
      "----------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel2\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrainloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtestloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m)\u001b[49m  \n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/utils.py:163\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(model, trainloader, device, testloader, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[1;32m    160\u001b[0m         optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# 统计\u001b[39;00m\n\u001b[0;32m--> 163\u001b[0m running_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m inputs\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    164\u001b[0m running_corrects \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m top1_correct\n\u001b[1;32m    165\u001b[0m running_top1_corrects \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m top1_correct\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, '90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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